Modifying Root-Zone Spoil Sampling Methods Using Vertical and Horizontal Composite Sampling Techniques

Sampling Techniques for Kernel Methods

We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels: sampling and quantization of the Gram matrix in training, randomized rounding in evaluating the kernel expansions, and random projections in evaluating the kernel itself. In all three cases, we give sharp bounds on the accuracy of the obtained approximations. Rather intriguingly, all three tech...

Composite sampling

Evaluating Composite Sampling Methods of Bacillus Spores at Low Concentrations

Restoring all facility operations after the 2001 Amerithrax attacks took years to complete, highlighting the need to reduce remediation time. Some of the most time intensive tasks were environmental sampling and sample analyses. Composite sampling allows disparate samples to be combined, with only a single analysis needed, making it a promising method to reduce response times. We developed a st...

Designing Efficient Distributed Algorithms Using Sampling Techniques

In this paper we show the power of sampling techniques in designing efficient distributed algorithms. In particular, we show that using sampling techniques, on some networks, selection can be done in such a way that the message complexity is independent of the cardinality of the set (file), provided the file size is polynomial in the network size. For example, given a file F of size n and an in...

Outlier overcoming using re-sampling techniques

Machine learning has extensively and successfully used statistical resampling techniques for generation of classifier and predictor ensembles. It has been frequently shown that combining so called unstable predictors has a stabilizing effect on and improves the performance of the prediction system generated in this way. In this paper we use the re-sampling techniques in the context of Principal...